"""

Created on Fri Mar 01 14:56:56 2013

Author: Josef Perktold
"""
import warnings

import numpy as np
from numpy.testing import (
    assert_allclose,
    assert_almost_equal,
    assert_array_less,
    assert_equal,
    assert_raises,
)
import pandas as pd
import pytest

import statsmodels.stats.proportion as smprop
from statsmodels.stats.proportion import (
    confint_proportions_2indep,
    multinomial_proportions_confint,
    power_proportions_2indep,
    proportion_confint,
    samplesize_proportions_2indep_onetail,
    score_test_proportions_2indep,
)
from statsmodels.tools.sm_exceptions import HypothesisTestWarning
from statsmodels.tools.testing import Holder
from statsmodels.stats.tests.results.results_proportion import res_binom, res_binom_methods

probci_methods = {'agresti_coull': 'agresti-coull',
                  'normal': 'asymptotic',
                  'beta': 'exact',
                  'wilson': 'wilson',
                  'jeffreys': 'bayes'
                  }

@pytest.mark.parametrize("case",res_binom)
@pytest.mark.parametrize("method",probci_methods)
def test_confint_proportion(method, case):
    count, nobs = case
    idx = res_binom_methods.index(probci_methods[method])
    res_low = res_binom[case].ci_low[idx]
    res_upp = res_binom[case].ci_upp[idx]
    if np.isnan(res_low) or np.isnan(res_upp):
        pytest.skip("Skipping due to NaN value")
    if (count == 0 or count == nobs) and method == 'jeffreys':
        # maybe a bug or different corner case definition
        pytest.skip("Skipping nobs 0 or count and jeffreys")
    if method == 'jeffreys' and nobs == 30:
        # something is strange in extreme case e.g 0/30 or 1/30
        pytest.skip("Skipping nobs is 30 and jeffreys due to extreme case problem")
    ci = proportion_confint(count, nobs, alpha=0.05, method=method)
    # we impose that confint is in [0, 1]
    res_low = max(res_low, 0)
    res_upp = min(res_upp, 1)
    assert_almost_equal(ci, [res_low, res_upp], decimal=6,
                        err_msg=repr(case) + method)


@pytest.mark.parametrize('method', probci_methods)
def test_confint_proportion_ndim(method):
    # check that it works with 1-D, 2-D and pandas

    count = np.arange(6).reshape(2, 3)
    nobs = 10 * np.ones((2, 3))

    count_pd = pd.DataFrame(count)
    nobs_pd =  pd.DataFrame(nobs)

    ci_arr = proportion_confint(count, nobs, alpha=0.05, method=method)
    ci_pd = proportion_confint(count_pd, nobs_pd, alpha=0.05,
                               method=method)
    assert_allclose(ci_arr, (ci_pd[0].values, ci_pd[1].values), rtol=1e-13)
    # spot checking one value
    ci12 = proportion_confint(count[1, 2], nobs[1, 2], alpha=0.05,
                              method=method)
    assert_allclose((ci_pd[0].values[1, 2], ci_pd[1].values[1, 2]), ci12,
                    rtol=1e-13)
    assert_allclose((ci_arr[0][1, 2], ci_arr[1][1, 2]), ci12, rtol=1e-13)

    # check that lists work as input
    ci_li = proportion_confint(count.tolist(), nobs.tolist(), alpha=0.05,
                               method=method)
    assert_allclose(ci_arr, (ci_li[0], ci_li[1]), rtol=1e-13)

    # check pandas Series, 1-D
    ci_pds = proportion_confint(count_pd.iloc[0], nobs_pd.iloc[0],
                                alpha=0.05, method=method)
    assert_allclose((ci_pds[0].values, ci_pds[1].values),
                    (ci_pd[0].values[0], ci_pd[1].values[0]), rtol=1e-13)

    # check scalar nobs, verifying one value
    ci_arr2 = proportion_confint(count, nobs[1, 2], alpha=0.05,
                                 method=method)
    assert_allclose((ci_arr2[0][1, 2], ci_arr[1][1, 2]), ci12, rtol=1e-13)

    # check floating point values
    ci_arr2 = proportion_confint(count + 1e-4, nobs[1, 2], alpha=0.05,
                                 method=method)
    # should be close to values with integer values
    assert_allclose((ci_arr2[0][1, 2], ci_arr[1][1, 2]), ci12, rtol=1e-4)


def test_samplesize_confidenceinterval_prop():
    #consistency test for samplesize to achieve confidence_interval
    nobs = 20
    ci = smprop.proportion_confint(12, nobs, alpha=0.05, method='normal')
    res = smprop.samplesize_confint_proportion(12./nobs, (ci[1] - ci[0]) / 2)
    assert_almost_equal(res, nobs, decimal=13)

def test_proportion_effect_size():
    # example from blog
    es = smprop.proportion_effectsize(0.5, 0.4)
    assert_almost_equal(es, 0.2013579207903309, decimal=13)

def test_confint_multinomial_proportions():
    from .results.results_multinomial_proportions import res_multinomial

    for ((method, description), values) in res_multinomial.items():
        cis = multinomial_proportions_confint(values.proportions, 0.05,
                                              method=method)
        assert_almost_equal(
            values.cis, cis, decimal=values.precision,
            err_msg=f'"{method}" method, {description}')

def test_multinomial_proportions_errors():
    # Out-of-bounds values for alpha raise a ValueError
    for alpha in [-.1, 0, 1, 1.1]:
        assert_raises(ValueError, multinomial_proportions_confint,
                      [5] * 50, alpha=alpha)

    assert_raises(ValueError, multinomial_proportions_confint,
                  np.arange(50) - 1)
    # Any unknown method is reported.
    for method in ['unknown_method', 'sisok_method', 'unknown-glaz']:
        assert_raises(NotImplementedError, multinomial_proportions_confint,
                      [5] * 50, method=method)

def test_confint_multinomial_proportions_zeros():
    # test when a count is zero or close to zero
    # values from R MultinomialCI
    ci01 = np.array([
     0.09364718, 0.1898413,
     0.00000000, 0.0483581,
     0.13667426, 0.2328684,
     0.10124019, 0.1974343,
     0.10883321, 0.2050273,
     0.17210833, 0.2683024,
     0.09870919, 0.1949033]).reshape(-1,2)

    ci0 = np.array([
    0.09620253, 0.19238867,
    0.00000000, 0.05061652,
    0.13924051, 0.23542664,
    0.10379747, 0.19998360,
    0.11139241, 0.20757854,
    0.17468354, 0.27086968,
    0.10126582, 0.19745196]).reshape(-1,2)

    # the shifts are the differences between "LOWER(SG)"  "UPPER(SG)" and
    # "LOWER(C+1)" "UPPER(C+1)" in verbose printout
    # ci01_shift = np.array([0.002531008, -0.002515122])  # not needed
    ci0_shift = np.array([0.002531642, 0.002515247])

    p = [56, 0.1, 73, 59, 62, 87, 58]
    ci_01 = smprop.multinomial_proportions_confint(p, 0.05,
                                                   method='sison_glaz')
    p = [56, 0, 73, 59, 62, 87, 58]
    ci_0 = smprop.multinomial_proportions_confint(p, 0.05,
                                                  method='sison_glaz')

    assert_allclose(ci_01, ci01, atol=1e-5)
    assert_allclose(ci_0, np.maximum(ci0 - ci0_shift, 0), atol=1e-5)
    assert_allclose(ci_01, ci_0, atol=5e-4)


class CheckProportionMixin:
    def test_proptest(self):
        # equality of k-samples
        pt = smprop.proportions_chisquare(self.n_success, self.nobs, value=None)
        assert_almost_equal(pt[0], self.res_prop_test.statistic, decimal=13)
        assert_almost_equal(pt[1], self.res_prop_test.p_value, decimal=13)

        # several against value
        pt = smprop.proportions_chisquare(self.n_success, self.nobs,
                                    value=self.res_prop_test_val.null_value[0])
        assert_almost_equal(pt[0], self.res_prop_test_val.statistic, decimal=13)
        assert_almost_equal(pt[1], self.res_prop_test_val.p_value, decimal=13)

        # one proportion against value
        pt = smprop.proportions_chisquare(self.n_success[0], self.nobs[0],
                                    value=self.res_prop_test_1.null_value)
        assert_almost_equal(pt[0], self.res_prop_test_1.statistic, decimal=13)
        assert_almost_equal(pt[1], self.res_prop_test_1.p_value, decimal=13)

    def test_pairwiseproptest(self):
        ppt = smprop.proportions_chisquare_allpairs(self.n_success, self.nobs,
                                  multitest_method=None)
        assert_almost_equal(ppt.pvals_raw, self.res_ppt_pvals_raw)
        ppt = smprop.proportions_chisquare_allpairs(self.n_success, self.nobs,
                                  multitest_method='h')
        assert_almost_equal(ppt.pval_corrected(), self.res_ppt_pvals_holm)

        pptd = smprop.proportions_chisquare_pairscontrol(self.n_success,
                                  self.nobs, multitest_method='hommel')
        assert_almost_equal(pptd.pvals_raw, ppt.pvals_raw[:len(self.nobs) - 1],
                            decimal=13)


    def test_number_pairs_1493(self):
        ppt = smprop.proportions_chisquare_allpairs(self.n_success[:3],
                                                    self.nobs[:3],
                                                    multitest_method=None)

        assert_equal(len(ppt.pvals_raw), 3)
        idx = [0, 1, 3]
        assert_almost_equal(ppt.pvals_raw, self.res_ppt_pvals_raw[idx])


class TestProportion(CheckProportionMixin):
    def setup_method(self):
        self.n_success = np.array([ 73,  90, 114,  75])
        self.nobs = np.array([ 86,  93, 136,  82])

        self.res_ppt_pvals_raw = np.array([
                 0.00533824886503131, 0.8327574849753566, 0.1880573726722516,
                 0.002026764254350234, 0.1309487516334318, 0.1076118730631731
                ])
        self.res_ppt_pvals_holm = np.array([
                 0.02669124432515654, 0.8327574849753566, 0.4304474922526926,
                 0.0121605855261014, 0.4304474922526926, 0.4304474922526926
                ])

        res_prop_test = Holder()
        res_prop_test.statistic = 11.11938768628861
        res_prop_test.parameter = 3
        res_prop_test.p_value = 0.011097511366581344
        res_prop_test.estimate = np.array([
             0.848837209302326, 0.967741935483871, 0.838235294117647,
             0.9146341463414634
            ]).reshape(4,1, order='F')
        res_prop_test.null_value = '''NULL'''
        res_prop_test.conf_int = '''NULL'''
        res_prop_test.alternative = 'two.sided'
        res_prop_test.method = '4-sample test for equality of proportions ' + \
                               'without continuity correction'
        res_prop_test.data_name = 'smokers2 out of patients'
        self.res_prop_test = res_prop_test

        #> pt = prop.test(smokers2, patients, p=rep(c(0.9), 4), correct=FALSE)
        #> cat_items(pt, "res_prop_test_val.")
        res_prop_test_val = Holder()
        res_prop_test_val.statistic = np.array([
             13.20305530710751
            ]).reshape(1,1, order='F')
        res_prop_test_val.parameter = np.array([
             4
            ]).reshape(1,1, order='F')
        res_prop_test_val.p_value = 0.010325090041836
        res_prop_test_val.estimate = np.array([
             0.848837209302326, 0.967741935483871, 0.838235294117647,
             0.9146341463414634
            ]).reshape(4,1, order='F')
        res_prop_test_val.null_value = np.array([
             0.9, 0.9, 0.9, 0.9
            ]).reshape(4,1, order='F')
        res_prop_test_val.conf_int = '''NULL'''
        res_prop_test_val.alternative = 'two.sided'
        res_prop_test_val.method = '4-sample test for given proportions without continuity correction'
        res_prop_test_val.data_name = 'smokers2 out of patients, null probabilities rep(c(0.9), 4)'
        self.res_prop_test_val = res_prop_test_val

        #> pt = prop.test(smokers2[1], patients[1], p=0.9, correct=FALSE)
        #> cat_items(pt, "res_prop_test_1.")
        res_prop_test_1 = Holder()
        res_prop_test_1.statistic = 2.501291989664086
        res_prop_test_1.parameter = 1
        res_prop_test_1.p_value = 0.113752943640092
        res_prop_test_1.estimate = 0.848837209302326
        res_prop_test_1.null_value = 0.9
        res_prop_test_1.conf_int = np.array([0.758364348004061,
                                             0.9094787701686766])
        res_prop_test_1.alternative = 'two.sided'
        res_prop_test_1.method = '1-sample proportions test without continuity correction'
        res_prop_test_1.data_name = 'smokers2[1] out of patients[1], null probability 0.9'
        self.res_prop_test_1 = res_prop_test_1

    # GH 2969
    def test_default_values(self):
        count = np.array([5, 12])
        nobs = np.array([83, 99])
        stat, pval = smprop.proportions_ztest(count, nobs, value=None)
        assert_almost_equal(stat, -1.4078304151258787)
        assert_almost_equal(pval, 0.15918129181156992)

    # GH 2779
    def test_scalar(self):
        count = 5
        nobs = 83
        value = 0.05
        stat, pval = smprop.proportions_ztest(count, nobs, value=value)
        assert_almost_equal(stat, 0.392126026314)
        assert_almost_equal(pval, 0.694965098115)

        assert_raises(ValueError, smprop.proportions_ztest, count, nobs, value=None)


def test_binom_test():
    #> bt = binom.test(51,235,(1/6),alternative="less")
    #> cat_items(bt, "binom_test_less.")
    binom_test_less = Holder()
    binom_test_less.statistic = 51
    binom_test_less.parameter = 235
    binom_test_less.p_value = 0.982022657605858
    binom_test_less.conf_int = [0, 0.2659460862574313]
    binom_test_less.estimate = 0.2170212765957447
    binom_test_less.null_value = 1. / 6
    binom_test_less.alternative = 'less'
    binom_test_less.method = 'Exact binomial test'
    binom_test_less.data_name = '51 and 235'

    #> bt = binom.test(51,235,(1/6),alternative="greater")
    #> cat_items(bt, "binom_test_greater.")
    binom_test_greater = Holder()
    binom_test_greater.statistic = 51
    binom_test_greater.parameter = 235
    binom_test_greater.p_value = 0.02654424571169085
    binom_test_greater.conf_int = [0.1735252778065201, 1]
    binom_test_greater.estimate = 0.2170212765957447
    binom_test_greater.null_value = 1. / 6
    binom_test_greater.alternative = 'greater'
    binom_test_greater.method = 'Exact binomial test'
    binom_test_greater.data_name = '51 and 235'

    #> bt = binom.test(51,235,(1/6),alternative="t")
    #> cat_items(bt, "binom_test_2sided.")
    binom_test_2sided = Holder()
    binom_test_2sided.statistic = 51
    binom_test_2sided.parameter = 235
    binom_test_2sided.p_value = 0.0437479701823997
    binom_test_2sided.conf_int = [0.1660633298083073, 0.2752683640289254]
    binom_test_2sided.estimate = 0.2170212765957447
    binom_test_2sided.null_value = 1. / 6
    binom_test_2sided.alternative = 'two.sided'
    binom_test_2sided.method = 'Exact binomial test'
    binom_test_2sided.data_name = '51 and 235'

    alltests = [('larger', binom_test_greater),
                ('smaller', binom_test_less),
                ('two-sided', binom_test_2sided)]

    for alt, res0 in alltests:
        # only p-value is returned
        res = smprop.binom_test(51, 235, prop=1. / 6, alternative=alt)
        #assert_almost_equal(res[0], res0.statistic)
        assert_almost_equal(res, res0.p_value, decimal=13)

    # R binom_test returns Copper-Pearson confint
    ci_2s = smprop.proportion_confint(51, 235, alpha=0.05, method='beta')
    ci_low, ci_upp = smprop.proportion_confint(51, 235, alpha=0.1,
                                               method='beta')
    assert_almost_equal(ci_2s, binom_test_2sided.conf_int, decimal=13)
    assert_almost_equal(ci_upp, binom_test_less.conf_int[1], decimal=13)
    assert_almost_equal(ci_low, binom_test_greater.conf_int[0], decimal=13)


def test_binom_rejection_interval():
    # consistency check with binom_test
    # some code duplication but limit checks are different
    alpha = 0.05
    nobs = 200
    prop = 12./20
    alternative='smaller'
    ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha,
                                                       alternative=alternative)
    assert_equal(ci_upp, nobs)
    pval = smprop.binom_test(ci_low, nobs, prop=prop,
                                  alternative=alternative)
    assert_array_less(pval, alpha)
    pval = smprop.binom_test(ci_low + 1, nobs, prop=prop,
                                  alternative=alternative)
    assert_array_less(alpha, pval)

    alternative='larger'
    ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha,
                                                       alternative=alternative)
    assert_equal(ci_low, 0)
    pval = smprop.binom_test(ci_upp, nobs, prop=prop,
                                  alternative=alternative)
    assert_array_less(pval, alpha)
    pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop,
                                  alternative=alternative)
    assert_array_less(alpha, pval)

    alternative='two-sided'
    ci_low, ci_upp = smprop.binom_test_reject_interval(prop, nobs, alpha=alpha,
                                                       alternative=alternative)
    pval = smprop.binom_test(ci_upp, nobs, prop=prop,
                             alternative=alternative)
    assert_array_less(pval, alpha)
    pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop,
                             alternative=alternative)
    assert_array_less(alpha, pval)
    pval = smprop.binom_test(ci_upp, nobs, prop=prop,
                             alternative=alternative)
    assert_array_less(pval, alpha)

    pval = smprop.binom_test(ci_upp - 1, nobs, prop=prop,
                             alternative=alternative)
    assert_array_less(alpha, pval)



def test_binom_tost():
    # consistency check with two different implementation,
    # proportion_confint is tested against R
    # no reference case from other package available
    ci = smprop.proportion_confint(10, 20, method='beta', alpha=0.1)
    bt = smprop.binom_tost(10, 20, *ci)
    assert_almost_equal(bt, [0.05] * 3, decimal=12)

    ci = smprop.proportion_confint(5, 20, method='beta', alpha=0.1)
    bt = smprop.binom_tost(5, 20, *ci)
    assert_almost_equal(bt, [0.05] * 3, decimal=12)

    # vectorized, TODO: observed proportion = 0 returns nan
    ci = smprop.proportion_confint(np.arange(1, 20), 20, method='beta',
                                   alpha=0.05)
    bt = smprop.binom_tost(np.arange(1, 20), 20, ci[0], ci[1])
    bt = np.asarray(bt)
    assert_almost_equal(bt, 0.025 * np.ones(bt.shape), decimal=12)

def test_power_binom_tost():
    # comparison numbers from PASS manual
    p_alt = 0.6 + np.linspace(0, 0.09, 10)
    power = smprop.power_binom_tost(0.5, 0.7, 500, p_alt=p_alt, alpha=0.05)
    res_power = np.array([0.9965,  0.9940,  0.9815,  0.9482,  0.8783,  0.7583,
                          0.5914,  0.4041,  0.2352,  0.1139])
    assert_almost_equal(power, res_power, decimal=4)

    rej_int = smprop.binom_tost_reject_interval(0.5, 0.7, 500)
    res_rej_int = (269, 332)
    assert_equal(rej_int, res_rej_int)

    # TODO: actual alpha=0.0489  for all p_alt above

    # another case
    nobs = np.arange(20, 210, 20)
    power = smprop.power_binom_tost(0.4, 0.6, nobs, p_alt=0.5, alpha=0.05)
    res_power = np.array([ 0.,  0.,  0.,  0.0889,  0.2356,  0.3517,  0.4457,
                           0.6154,  0.6674,  0.7708])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

def test_power_ztost_prop():
    power = smprop.power_ztost_prop(0.1, 0.9, 10, p_alt=0.6, alpha=0.05,
                         discrete=True, dist='binom')[0]
    assert_almost_equal(power, 0.8204, decimal=4) # PASS example

    with warnings.catch_warnings():  # python >= 2.6
        warnings.simplefilter("ignore", HypothesisTestWarning)
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=False,
                                        dist='binom')[0]

        res_power = np.array([ 0., 0., 0., 0.0889, 0.2356, 0.4770, 0.5530,
            0.6154,  0.7365,  0.7708])
        # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
        assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

        # with critval_continuity correction
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=False,
                                        dist='binom', variance_prop=None,
                                        continuity=2, critval_continuity=1)[0]

        res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457,
                              0.6154, 0.6674, 0.7708])
        # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
        assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=False,
                                        dist='binom', variance_prop=0.5,
                                        critval_continuity=1)[0]

        res_power = np.array([0., 0., 0., 0.0889, 0.2356, 0.3517, 0.4457,
                              0.6154, 0.6674, 0.7112])
        # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
        assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)


def test_ztost():
    xfair = np.repeat([1, 0], [228, 762-228])

    # comparing to SAS last output at
    # http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm
    # confidence interval for tost
    # generic ztost is moved to weightstats
    from statsmodels.stats.weightstats import zconfint, ztost
    ci01 = zconfint(xfair, alpha=0.1, ddof=0)
    assert_almost_equal(ci01,  [0.2719, 0.3265], 4)
    res = ztost(xfair, 0.18, 0.38, ddof=0)

    assert_almost_equal(res[1][0], 7.1865, 4)
    assert_almost_equal(res[2][0], -4.8701, 4)
    assert_array_less(res[0], 0.0001)


def test_power_ztost_prop_norm():
    # regression test for normal distribution
    # from a rough comparison, the results and variations look reasonable
    with pytest.warns(HypothesisTestWarning):
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=False,
                                        dist='norm', variance_prop=0.5,
                                        continuity=0, critval_continuity=0)[0]

    res_power = np.array([0., 0., 0., 0.11450013, 0.27752006, 0.41495922,
                          0.52944621, 0.62382638, 0.70092914, 0.76341806])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

    # regression test for normal distribution
    with pytest.warns(HypothesisTestWarning):
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=False,
                                        dist='norm', variance_prop=0.5,
                                        continuity=1, critval_continuity=0)[0]

    res_power = np.array([0., 0., 0.02667562, 0.20189793, 0.35099606,
                          0.47608598, 0.57981118, 0.66496683, 0.73427591,
                          0.79026127])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

    # regression test for normal distribution
    with pytest.warns(HypothesisTestWarning):
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=True,
                                        dist='norm', variance_prop=0.5,
                                        continuity=1, critval_continuity=0)[0]

    res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
                          0.55312718, 0.61549537, 0.66743625, 0.77066806])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

    # regression test for normal distribution
    with pytest.warns(HypothesisTestWarning):
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=True,
                                        dist='norm', variance_prop=0.5,
                                        continuity=1, critval_continuity=1)[0]

    res_power = np.array([0., 0., 0., 0.08902071, 0.23582284, 0.35192313,
                          0.44588687, 0.61549537, 0.66743625, 0.71115563])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)

    # regression test for normal distribution
    with pytest.warns(HypothesisTestWarning):
        power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
                                        p_alt=0.5, alpha=0.05, discrete=True,
                                        dist='norm', variance_prop=None,
                                        continuity=0, critval_continuity=0)[0]

    res_power = np.array([0., 0., 0., 0., 0.15851942, 0.41611758,
                          0.5010377, 0.5708047, 0.70328247, 0.74210096])
    # TODO: I currently do not impose power>=0, i.e np.maximum(power, 0)
    assert_almost_equal(np.maximum(power, 0), res_power, decimal=4)


def test_proportion_ztests():
    # currently only consistency test with proportions chisquare
    # Note: alternative handling is generic

    res1 = smprop.proportions_ztest(15, 20., value=0.5, prop_var=0.5)
    res2 = smprop.proportions_chisquare(15, 20., value=0.5)
    assert_almost_equal(res1[1], res2[1], decimal=13)

    res1 = smprop.proportions_ztest(np.asarray([15, 10]),
                                    np.asarray([20., 20]),
                                    value=0, prop_var=None)
    res2 = smprop.proportions_chisquare(np.asarray([15, 10]),
                                        np.asarray([20., 20]))
    # test only p-value
    assert_almost_equal(res1[1], res2[1], decimal=13)

    # test with integers, issue #7603
    res1 = smprop.proportions_ztest(np.asarray([15, 10]),
                                    np.asarray([20, 50000]),
                                    value=0, prop_var=None)
    res2 = smprop.proportions_chisquare(np.asarray([15, 10]),
                                        np.asarray([20, 50000]))
    # test only p-value
    assert_almost_equal(res1[1], res2[1], decimal=13)
    assert_array_less(0, res2[-1][1])  # expected should be positive


def test_confint_2indep():
    # alpha = 0.05
    count1, nobs1 = 7, 34
    count2, nobs2 = 1, 34

    # result tables from Fagerland et al 2015
    '''
    diff:
    Wald 0.029 0.32 0.29
    Agresti–Caffo 0.012 0.32 0.31
    Newcombe hybrid score 0.019 0.34 0.32
    Miettinen–Nurminen asymptotic score 0.028 0.34 0.31
    Santner–Snell exact unconditional -0.069 0.41 0.48
    Chan–Zhang exact unconditional 0.019 0.36 0.34
    Agresti–Min exact unconditional 0.024 0.35 0.33

    ratio:
    Katz log 0.91 54 4.08
    Adjusted log 0.92 27 3.38
    Inverse sinh 1.17 42 3.58
    Koopman asymptotic score 1.21 43 3.57
    Chan–Zhang 1.22 181 5.00
    Agresti–Min 1.15 89 4.35

    odds-ratio
    Woolf logit 0.99 74 4.31
    Gart adjusted logit 0.98 38 3.65
    Independence-smoothed logit 0.99 60 4.11
    Cornfield exact conditional 0.97 397 6.01
    Cornfield mid-p 1.19 200 5.12
    Baptista–Pike exact conditional 1.00 195 5.28
    Baptista–Pike mid-p 1.33 99 4.31
    Agresti–Min exact unconditional 1.19 72 4.10
    '''  # pylint: disable=W0105
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    method='newcomb',
                                    compare='diff', alpha=0.05)
    # one decimal to upp added from regression result
    assert_allclose(ci, [0.019, 0.340], atol=0.005)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    method='wald',
                                    compare='diff', alpha=0.05)
    assert_allclose(ci, [0.029, 0.324], atol=0.005)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    method='agresti-caffo',
                                    compare='diff', alpha=0.05)
    assert_allclose(ci, [0.012, 0.322], atol=0.005)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='diff',
                                    method='score', correction=True)
    assert_allclose(ci, [0.028, 0.343], rtol=0.03)

    # ratio
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='ratio',
                                    method='log')
    assert_allclose(ci, [0.91, 54], rtol=0.01)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='ratio',
                                    method='log-adjusted')
    assert_allclose(ci, [0.92, 27], rtol=0.01)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='ratio',
                                    method='score', correction=False)
    assert_allclose(ci, [1.21, 43], rtol=0.01)

    # odds-ratio
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='or',
                                    method='logit')
    assert_allclose(ci, [0.99, 74], rtol=0.01)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='or',
                                    method='logit-adjusted')
    assert_allclose(ci, [0.98, 38], rtol=0.01)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='or',
                                    method='logit-smoothed')
    assert_allclose(ci, [0.99, 60], rtol=0.01)
    ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                    compare='odds-ratio',
                                    method='score', correction=True)
    # regression test
    assert_allclose(ci, [1.246622, 56.461576], rtol=0.01)


def test_confint_2indep_propcis():
    # unit tests compared to R package PropCis
    # alpha = 0.05
    count1, nobs1 = 7, 34
    count2, nobs2 = 1, 34

    # > library(PropCIs)
    # > diffscoreci(7, 34, 1, 34, 0.95)
    ci = 0.0270416, 0.3452912
    ci1 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                     compare="diff",
                                     method="score", correction=True)
    assert_allclose(ci1, ci, atol=0.002)  # lower agreement (iterative)
    # > wald2ci(7, 34, 1, 34, 0.95, adjust="AC")
    ci = 0.01161167, 0.32172166
    ci1 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                     compare="diff",
                                     method="agresti-caffo")
    assert_allclose(ci1, ci, atol=6e-7)
    # > wald2ci(7, 34, 1, 34, 0.95, adjust="Wald")
    ci = 0.02916942, 0.32377176
    ci1 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                     compare="diff",
                                     method="wald", correction=False)
    assert_allclose(ci1, ci, atol=6e-7)

    # > orscoreci(7, 34, 1, 34, 0.95)
    ci = 1.246309, 56.486130
    ci1 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                     compare="odds-ratio",
                                     method="score", correction=True)
    assert_allclose(ci1, ci, rtol=5e-4)  # lower agreement (iterative)

    # > riskscoreci(7, 34, 1, 34, 0.95)
    ci = 1.220853, 42.575718
    ci1 = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                     compare="ratio",
                                     method="score", correction=False)
    assert_allclose(ci1, ci, atol=6e-7)


def test_score_test_2indep():
    # this does not verify the statistic and pvalue yet
    count1, nobs1 = 7, 34
    count2, nobs2 = 1, 34

    for co in ['diff', 'ratio', 'or']:
        res = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
                                            compare=co)
        assert_allclose(res.prop1_null, res.prop2_null, rtol=1e-10)

        # check that equality case is handled
        val = 0 if co == 'diff' else 1.
        s0, pv0 = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
                                                compare=co, value=val,
                                                return_results=False)[:2]
        s1, pv1 = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
                                                compare=co, value=val + 1e-10,
                                                return_results=False)[:2]
        assert_allclose(s0, s1, rtol=1e-8)
        assert_allclose(pv0, pv1, rtol=1e-8)
        s1, pv1 = score_test_proportions_2indep(count1, nobs1, count2, nobs2,
                                                compare=co, value=val - 1e-10,
                                                return_results=False)[:2]
        assert_allclose(s0, s1, rtol=1e-8)
        assert_allclose(pv0, pv1, rtol=1e-8)


def test_test_2indep():
    # this checks the pvalue of the hypothesis test at value equal to the
    # confidence limit
    alpha = 0.05
    count1, nobs1 = 7, 34
    count2, nobs2 = 1, 34

    methods_both = [
                    ('diff', 'agresti-caffo'),
                    # ('diff', 'newcomb'),  # only confint
                    ('diff', 'score'),
                    ('diff', 'wald'),
                    ('ratio', 'log'),
                    ('ratio', 'log-adjusted'),
                    ('ratio', 'score'),
                    ('odds-ratio', 'logit'),
                    ('odds-ratio', 'logit-adjusted'),
                    ('odds-ratio', 'logit-smoothed'),
                    ('odds-ratio', 'score'),
                    ]

    for co, method in methods_both:
        low, upp = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                              compare=co, method=method,
                                              alpha=alpha, correction=False)

        res = smprop.test_proportions_2indep(
                count1, nobs1, count2, nobs2, value=low, compare=co,
                method=method, correction=False)
        assert_allclose(res.pvalue, alpha, atol=1e-10)

        res = smprop.test_proportions_2indep(
                count1, nobs1, count2, nobs2, value=upp, compare=co,
                method=method, correction=False)
        assert_allclose(res.pvalue, alpha, atol=1e-10)

        _, pv = smprop.test_proportions_2indep(
                    count1, nobs1, count2, nobs2, value=upp, compare=co,
                    method=method, alternative='smaller',
                    correction=False, return_results=False)
        assert_allclose(pv, alpha / 2, atol=1e-10)

        _, pv = smprop.test_proportions_2indep(
                    count1, nobs1, count2, nobs2, value=low, compare=co,
                    method=method, alternative='larger',
                    correction=False, return_results=False)
        assert_allclose(pv, alpha / 2, atol=1e-10)

    # test Miettinen/Nurminen small sample correction
    co, method = 'ratio', 'score'
    low, upp = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                          compare=co, method=method,
                                          alpha=alpha, correction=True)

    res = smprop.test_proportions_2indep(
            count1, nobs1, count2, nobs2, value=low, compare=co,
            method=method, correction=True)
    assert_allclose(res.pvalue, alpha, atol=1e-10)


def test_equivalence_2indep():
    # this checks the pvalue of the equivalence test at value equal to the
    # confidence limit
    alpha = 0.05
    count1, nobs1 = 7, 34
    count2, nobs2 = 1, 34
    count1v, nobs1v = [7, 1], 34
    count2v, nobs2v = [1, 7], 34

    methods_both = [
                    ('diff', 'agresti-caffo'),
                    # ('diff', 'newcomb'),  # only confint
                    ('diff', 'score'),
                    ('diff', 'wald'),
                    ('ratio', 'log'),
                    ('ratio', 'log-adjusted'),
                    ('ratio', 'score'),
                    ('odds-ratio', 'logit'),
                    ('odds-ratio', 'logit-adjusted'),
                    ('odds-ratio', 'logit-smoothed'),
                    ('odds-ratio', 'score'),
                    ]

    for co, method in methods_both:
        low, upp = confint_proportions_2indep(count1, nobs1, count2, nobs2,
                                              compare=co, method=method,
                                              alpha=2 * alpha,
                                              correction=False)

        # Note: test should have only one margin at confint
        res = smprop.tost_proportions_2indep(
                count1, nobs1, count2, nobs2, low, upp * 1.05, compare=co,
                method=method, correction=False)
        assert_allclose(res.pvalue, alpha, atol=1e-10)

        res = smprop.tost_proportions_2indep(
                count1, nobs1, count2, nobs2, low * 0.95, upp, compare=co,
                method=method, correction=False)
        assert_allclose(res.pvalue, alpha, atol=1e-10)

        # vectorized
        if method == 'logit-smoothed':
            # not correctly vectorized
            return
        res1 = res  # for debugging  # noqa
        res = smprop.tost_proportions_2indep(
                count1v, nobs1v, count2v, nobs2v, low * 0.95, upp, compare=co,
                method=method, correction=False)
        assert_allclose(res.pvalue[0], alpha, atol=1e-10)


def test_score_confint_koopman_nam():

    # example Koopman, based on Nam 1995

    x0, n0 = 16, 80
    x1, n1 = 36, 40
    # x = x0 + x1
    # n = n0 + n1
    # p0 = x0 / n0
    # p1 = x1 / n1

    results_nam = Holder()
    results_nam.p0_roots = [0.1278, 0.2939, 0.4876]
    results_nam.conf_int = [2.940, 7.152]

    res = smprop._confint_riskratio_koopman(x1, n1, x0, n0,  alpha=0.05)

    assert_allclose(res._p_roots, results_nam.p0_roots, atol=4)
    assert_allclose(res.confint, results_nam.conf_int, atol=3)

    table = [67, 9, 7, 16]  # [67, 7, 9, 16]
    resp = smprop._confint_riskratio_paired_nam(table, alpha=0.05)
    # TODO: currently regression test, need verified results
    ci_old = [0.917832,  1.154177]
    assert_allclose(resp.confint, ci_old, atol=3)


def test_power_2indep():
    # test against R
    pow_ = power_proportions_2indep(-0.25, 0.75, 76.70692)
    assert_allclose(pow_.power, 0.9, atol=1e-8)

    n = samplesize_proportions_2indep_onetail(-0.25, 0.75, 0.9, ratio=1,
                                              alpha=0.05, value=0,
                                              alternative='two-sided')
    assert_allclose(n, 76.70692, atol=1e-5)

    power_proportions_2indep(-0.25, 0.75, 62.33551, alternative="smaller")
    assert_allclose(pow_.power, 0.9, atol=1e-8)

    pow_ = power_proportions_2indep(0.25, 0.5, 62.33551, alternative="smaller")
    assert_array_less(pow_.power, 0.05)

    pow_ = power_proportions_2indep(0.25, 0.5, 62.33551, alternative="larger",
                                    return_results=False)
    assert_allclose(pow_, 0.9, atol=1e-8)

    pow_ = power_proportions_2indep(-0.15, 0.65, 83.4373, return_results=False)
    assert_allclose(pow_, 0.5, atol=1e-8)

    n = samplesize_proportions_2indep_onetail(-0.15, 0.65, 0.5, ratio=1,
                                              alpha=0.05, value=0,
                                              alternative='two-sided')

    assert_allclose(n, 83.4373, atol=0.05)

    # Stata example
    from statsmodels.stats.power import normal_sample_size_one_tail
    res = power_proportions_2indep(-0.014, 0.015, 550, ratio=1.)
    assert_allclose(res.power, 0.7415600, atol=1e-7)
    n = normal_sample_size_one_tail(-0.014, 0.7415600, 0.05 / 2,
                                    std_null=res.std_null,
                                    std_alternative=res.std_alt)
    assert_allclose(n, 550, atol=0.05)
    n2 = samplesize_proportions_2indep_onetail(-0.014, 0.015, 0.7415600,
                                               ratio=1, alpha=0.05, value=0,
                                               alternative='two-sided')
    assert_allclose(n2, n, rtol=1e-13)

    # with nobs ratio != 1
    # note Stata has reversed ratio compared to ours, see #8049
    pwr_st = 0.7995659211532175
    n = 154
    res = power_proportions_2indep(-0.1, 0.2, n, ratio=2.)
    assert_allclose(res.power, pwr_st, atol=1e-7)

    n2 = samplesize_proportions_2indep_onetail(-0.1, 0.2, pwr_st, ratio=2)
    assert_allclose(n2, n, rtol=1e-4)


@pytest.mark.parametrize("count", np.arange(10, 90, 5))
@pytest.mark.parametrize(
    "method", list(probci_methods.keys()) + ["binom_test"]
)
@pytest.mark.parametrize("array_like", [False, True])
def test_ci_symmetry(count, method, array_like):
    _count = [count] * 3 if array_like else count
    n = 100
    a = proportion_confint(count, n, method=method)
    b = proportion_confint(n - count, n, method=method)
    assert_allclose(np.array(a), 1.0 - np.array(b[::-1]))


@pytest.mark.parametrize("nobs", [47, 50])
@pytest.mark.parametrize("count", np.arange(48))
@pytest.mark.parametrize("array_like", [False, True])
def test_ci_symmetry_binom_test(nobs, count, array_like):
    _count = [count] * 3 if array_like else count
    nobs_m_count = [nobs - count] * 3 if array_like else nobs - count
    a = proportion_confint(_count, nobs, method="binom_test")
    b = proportion_confint(nobs_m_count, nobs, method="binom_test")
    assert_allclose(np.array(a), 1.0 - np.array(b[::-1]))


def test_int_check():
    # integer values are required only if method="binom_test"
    with pytest.raises(ValueError):
        proportion_confint(10.5, 20, method="binom_test")
    with pytest.raises(ValueError):
        proportion_confint(10, 20.5, method="binom_test")
    with pytest.raises(ValueError):
        proportion_confint(np.array([10.3]), 20, method="binom_test")

    a = proportion_confint(21.0, 47, method="binom_test")
    b = proportion_confint(21, 47, method="binom_test")
    c = proportion_confint(21, 47.0, method="binom_test")
    assert_allclose(a, b)
    assert_allclose(a, c)


@pytest.mark.parametrize("count", np.arange(10, 90, 5))
@pytest.mark.parametrize(
    "method", list(probci_methods.keys()) + ["binom_test"]
)
def test_ci_symmetry_array(count, method):
    n = 100
    a = proportion_confint([count, count], n, method=method)
    b = proportion_confint([n - count, n - count], n, method=method)
    assert_allclose(np.array(a), 1.0 - np.array(b[::-1]))
